Can I Run QwQ 32B on a NVIDIA B100?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
32.0B
GPU memory
192GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 192GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 65.0 GB | 66.5 GB | 64.0 GB | +127.0 GB |
| Q8_0 | 35.0 GB | 36.5 GB | 34.0 GB | +157.0 GB |
| Q6_K | 27.4 GB | 28.9 GB | 26.4 GB | +164.6 GB |
| Q5_K_M | 23.7 GB | 25.2 GB | 22.7 GB | +168.3 GB |
| Q4_K_M | 20.4 GB | 21.9 GB | 19.4 GB | +171.6 GB |
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Full model details
QwQ 32B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA B100 →
Top-ranked open-source models that fit in 192GB.
FAQ
Can the NVIDIA B100 run QwQ 32B?
Yes. The NVIDIA B100's 192GB of VRAM is enough to run QwQ 32B at fp16 quantization (65.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 192GB. It uses about 65.0GB of memory and 66.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.